数据分析是指用适当的统计方法对收集来的大量第一手资料和第二手资料进行分析,以求最大化地开发数据资料的功能,发挥数据的作用。

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这本书涵盖了用R总结数据的基本探索性技术。这些技术通常在正式建模开始之前应用,可以帮助开发更复杂的统计模型。探索技术对于消除或强化关于世界的潜在假设也很重要,这些假设可以通过你所拥有的数据来解决。我们将详细介绍R中的绘图系统以及构造信息数据图形的一些基本原则。我们还将介绍一些用于可视化高维数据的常见多元统计技术。

这本书教你使用R来有效地可视化和探索复杂的数据集。探索性数据分析是数据科学过程的一个关键部分,因为它允许您尖锐地提出问题并改进建模策略。这本书是基于行业领先的约翰霍普金斯数据科学专业,最广泛订阅的数据科学培训项目创建。

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Feature selection techniques are essential for high-dimensional data analysis. In the last two decades, their popularity has been fuelled by the increasing availability of high-throughput biomolecular data where high-dimensionality is a common data property. Recent advances in biotechnologies enable global profiling of various molecular and cellular features at single-cell resolution, resulting in large-scale datasets with increased complexity. These technological developments have led to a resurgence in feature selection research and application in the single-cell field. Here, we revisit feature selection techniques and summarise recent developments. We review their versatile application to a range of single-cell data types including those generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions on which feature selection could have a significant impact. Finally, we consider the scalability and make general recommendations on the utility of each type of feature selection method. We hope this review serves as a reference point to stimulate future research and application of feature selection in the single-cell era.

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